Detecting anomalies in temporal data is challenging due to anomalies being dependent on temporal dynamics. One-class classification methods are commonly used for anomaly detection tasks, but they have limitations when applied to temporal data. In particular, mapping all normal instances into a single hypersphere to capture their global characteristics can lead to poor performance in detecting context-based anomalies where the abnormality is defined with respect to local information. To address this limitation, we propose a novel approach inspired by the loss function of DeepSVDD. Instead of mapping all normal instances into a single hypersphere center, each normal instance is pulled toward a recent context window. However, this approach is prone to a representation collapse issue where the neural network that encodes a given instance and its context is optimized towards a constant encoder solution. To overcome this problem, we combine our approach with a deterministic contrastive loss from Neutral AD, a promising self-supervised learning anomaly detection approach. We provide a theoretical analysis to demonstrate that the incorporation of the deterministic contrastive loss can effectively prevent the occurrence of a constant encoder solution. Experimental results show superior performance of our model over various baselines and model variants on real-world industrial datasets.
